You can use the BigQuery web UI in the Cloud Console as a visual interface to complete tasks like running queries, loading data, and exporting data. This quickstart shows you how to query tables in a public dataset and how to load sample data into BigQuery using the Cloud Console.
Before you begin
Sign in to your Google Account.
If you don't already have one, sign up for a new account.
In the Cloud Console, on the project selector page, select or create a Cloud project.
- BigQuery is automatically enabled in new projects. To activate BigQuery in a preexisting project, go to Enable the BigQuery API.
- BigQuery provides a sandbox if you do not want to provide a credit card or enable billing for your project. The steps in this topic work for a project whether or not your project has billing enabled. If you optionally want to enable billing, see Learn how to enable billing.
Query a public dataset
The BigQuery web UI provides an interface to query tables, including public datasets offered by BigQuery.
In this example, you query the USA Name Data public dataset to determine the most common names in the US between 1910 and 2013.
BigQuery public datasets are displayed by default in the Cloud Console. To open the public datasets project manually, enter the following URL in your browser.
To query data in a public dataset, follow these steps:
In the Cloud Console, go to the BigQuery web UI.
Click Compose new query. If this text is dimmed, then the Query editor is already open.
Copy and paste the following query into the query text area.
SELECT name, gender, SUM(number) AS total FROM `bigquery-public-data.usa_names.usa_1910_2013` GROUP BY name, gender ORDER BY total DESC LIMIT 10
To view the query validator, click the green check mark.
If the query is valid, a green check mark appears. If the query is invalid, a red exclamation point appears. If the query is valid, the validator also shows the amount of data the query will process when you run it. The data processed is helpful for determining the cost of running the query.
Click Run. The query results page appears below the query window. At the top of the query results page, the time elapsed and the data processed by the query are displayed. Below the
Query complete...message, a table displays the query results with a header row containing the name of each column you selected in the query.
Load data into a table
Next, load data into a table and query it.
Download the data
The file you're downloading contains approximately 7 MB of data about popular baby names, and it is provided by the US Social Security Administration.
Download the baby names zip file.
Extract the file onto your machine.
The zip file contains a
NationalReadMe.pdffile that describes the dataset. Learn more about the dataset.
Open the file named
yob2014.txtto see what the data looks like. The file is a comma-separated value (CSV) file with the following three columns: name, sex (
F), and number of children with that name. The file has no header row.
Note the location of the
yob2014.txtfile so that you can find it later.
Create a dataset
Next, create a dataset in the web UI to store the data.
If necessary, open the BigQuery web UI.
In the navigation panel, in the Resources section, click your project name.
On the right side, in the details panel, click Create dataset.
On the Create dataset page, do the following:
- For Dataset ID, enter
For Data location, choose United States (US). Currently, the public datasets are stored in the
USmulti-region location. For simplicity, place your dataset in the same location.
- For Dataset ID, enter
Leave all of the other default settings in place and click Create dataset.
Load the data into a new table
Next, load the data into a new table.
In the navigation panel, in the Resources section, click the babynames dataset that you just created.
On the right side, in the details panel, click Create table.
Use the default values for all settings unless otherwise indicated.
On the Create table page, do the following:
- For Source, click Empty table and choose Upload.
- For Select file, click Browse, navigate to the
yob2014.txtfile, and click Open.
- For File format, click Avro and choose CSV.
- In the Destination section, for Table name, enter
In the Schema section, click the Edit as text toggle and paste the following schema definition into the box.
Click Create table.
Wait for BigQuery to create the table and load the data. While BigQuery loads the data, a (1 running) string displays beside the job history in the navigation panel. The string disappears after the data is loaded.
Preview the table
After the (1 running) string disappears, you can access the table. To preview the first few rows of the data, follow these steps:
In the navigation panel, select babynames > names_2014.
In the details panel, click the Preview tab.
Query the table
Now that you've loaded data into a table, you can query it. The process is identical to the previous example, except that this time, you're querying your table instead of a public table.
If necessary, click the Compose new query button. Unless you hid the query window previously, it should still be visible.
Copy and paste the following query into the query text area. This query retrieves the top five baby names for US males in 2014.
SELECT name, count FROM `babynames.names_2014` WHERE gender = 'M' ORDER BY count DESC LIMIT 5
Click Run. The results are displayed below the query window.
To avoid incurring charges to your Google Cloud account for the resources used in this quickstart, follow these steps.
If necessary, open the BigQuery web UI.
In the navigation panel, in the Resources section, click the babynames dataset you created.
In the details panel, on the right side, click Delete dataset. This action deletes the dataset, the table, and all the data.
In the Delete dataset dialog box, confirm the delete command by typing the name of your dataset (
babynames) and then click Delete.
To learn more about the BigQuery web UI in the Cloud Console, see Using the BigQuery web UI in the Cloud Console.
To learn how to load a JSON file with nested and repeated data, see Loading nested and repeated JSON data.
To learn more about loading data into BigQuery, see Introduction to loading data.
To learn more about querying data, see Overview of querying BigQuery data.